Prediction of concrete strength with data mining methods using artificial bee colony as feature selector
| dc.contributor.author | Kaya Keleş, Mümine | |
| dc.contributor.author | Keleş, Abdullah Emre | |
| dc.contributor.author | Kiliç, Ümit | |
| dc.date.accessioned | 2025-01-06T17:29:43Z | |
| dc.date.available | 2025-01-06T17:29:43Z | |
| dc.date.issued | 2019 | |
| dc.description | 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018 -- 28 September 2018 through 30 September 2018 -- Malatya -- 144523 | |
| dc.description.abstract | Concrete which is a highly complex material is the most basic input of the construction industry. Because of its strength, concrete is one of the most preferred structural building materials. In the ready-mixed concrete sector, there is an increasing need for earthquake resistant structures due to the fact that some producers produce out of control and poor quality. Ready-mixed concrete is a product whose quality can only be understood at the end of the 28th day if it is only controlled by taking the sample by the user. In this study, a data mining study was conducted on the factors affecting the 28-day compressive strength of concrete using the Concrete Slump Test Data Set from UCI Machine Learning Repository. The Artificial Bee Colony Algorithm is used as a feature selection method in order to determine the important ones of the concrete components, which are cement, slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate, affecting concrete strength and tried to predict the strength with data mining algorithms. As a result of the study, it was observed that Random Forest Algorithm gave the highest success rate with 91.2621% accuracy using only 3 features, which are cement, fly ash, and water. This means that it is possible to predict the compressive strength of concrete with a ratio above 90% by using a smaller number of concrete components. © 2018 IEEE. | |
| dc.description.sponsorship | Scientific Research Projects Commission Unit of Adana Science and Technology University, (18103004, 18332001) | |
| dc.identifier.doi | 10.1109/IDAP.2018.8620905 | |
| dc.identifier.isbn | 978-153866878-8 | |
| dc.identifier.scopus | 2-s2.0-85062486623 | |
| dc.identifier.uri | https://doi.org/10.1109/IDAP.2018.8620905 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14669/1313 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | 2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018 | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_20241211 | |
| dc.subject | 28-Day compressive strength | |
| dc.subject | Artificial Bee Colony | |
| dc.subject | Data Mining | |
| dc.subject | Feature Selection | |
| dc.subject | Prediction of concrete strength | |
| dc.title | Prediction of concrete strength with data mining methods using artificial bee colony as feature selector | |
| dc.type | Conference Object |
